oneapi-src/distributed-ranges: Distributed ranges is a generalization of C++ ranges for distributed data structures. · GitHub

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Productivity library for distributed and partitioned memory based on C++ Ranges.

Distributed Ranges is a C++ productivity library for distributed and partitioned memory based on C++ ranges. It offers a collection of data structures, views, and algorithms for building generic abstractions and provides interoperability with MPI, SHMEM, SYCL and OpenMP and portability on CPUs and GPUs. NUMA-aware allocators and distributed data structures facilitate development of C++ applications on heterogeneous nodes with multiple devices and achieve excellent performance and parallel scalability by exploiting local compute and data access.

In this model one can:

• create a distributed data structure that work with all our algorithms out of the box
• create an algorithm that works with all our distributed data structures out of the box

Distributed Ranges is a glue that makes this possible.

• Usage:
  • Introductory presentation: Distributed Ranges, why you need it, 2024
  • Article: Get Started with Distributed Ranges, 2023
  • Tutorial: Distributed Ranges Tutorial
• Design / Implementation:
  • Conference paper: Distributed Ranges, A Model for Distributed Data Structures, Algorithms, and Views, 2024
  • Talk: CppCon 2023; Benjamin Brock; Distributed Ranges, 2023
  • Technical presentation: Intel Innovation'23, 2023
  • API specification

• Linux
• cmake >=3.20
• OneAPI HPC Toolkit installed

Enable OneAPI by:

source ~/intel/oneapi/setvars.sh

... or by:

source /opt/intel/oneapi/setvars.sh

... or wherever you have oneapi/setvars.sh script installed in your system.

• CUDA
• OneAPI for NVIDIA GPUs plugin

When enabling OneAPI use --include-intel-llvm option, e.g. call:

source ~/intel/oneapi/setvars.sh --include-intel-llvm

... instead of source ~/intel/oneapi/setvars.sh.

All tests and examples can be build by:

CXX=icpx cmake -B build
cmake --build build -- -j

To build multi-process tests call:

CXX=icpx cmake -B build -DENABLE_CUDA:BOOL=ON
cmake --build build --target mp-all-tests -- -j

Run multi process tests:

ctest --test-dir build --output-on-failure -L MP -j 4

Run single process tests:

ctest --test-dir build --output-on-failure -L SP -j 4

Run all tests:

ctest --test-dir build --output-on-failure -L TESTLABEL -j 4

Two binaries are build for benchmarks:

• mp-bench - for benchmarking Multi-Process model
• sp-bench - for benchmarking Single-Process model

Here are examples of running single benchmarks.

Running GemvEq_DR strong scaling benchmark in Multi-Process model using two GPUs:

ONEAPI_DEVICE_SELECTOR='level_zero:gpu' I_MPI_OFFLOAD=1 I_MPI_OFFLOAD_CELL_LIST=0-11 \
mpiexec -n 2 -ppn 2  build/benchmarks/gbench/mp/mp-bench --vector-size 1000000000 --reps 50 \
--v=3 --benchmark_out=mp_gemv.txt --benchmark_filter=GemvEq_DR/ --sycl

Running Exclusive_Scan_DR weak scaling in Single-Process model using two GPUs:

ONEAPI_DEVICE_SELECTOR='level_zero:gpu' KMP_AFFINITY=compact \
build/benchmarks/gbench/sp/sp-bench --vector-size 1000000000 --reps 50 \
--v=3 --benchmark_out=sp_exclscan.txt --benchmark_filter=Exclusive_Scan_DR/ \
--weak-scaling --device-memory --num-devices 2

Check all options:

./build/benchmarks/gbench/mp/mp-bench --help  # see google test options help
./build/benchmarks/gbench/mp/mp-bench --drhelp  # see DR specific options

See Distributed Ranges Tutorial for a few well explained examples.

If your project uses CMAKE, add the following to your CMakeLists.txt to download the library:

find_package(MPI REQUIRED)
include(FetchContent)
FetchContent_Declare(
  dr
  GIT_REPOSITORY https://github.com/oneapi-src/distributed-ranges.git
  GIT_TAG main
  )
FetchContent_MakeAvailable(dr)

The above will define targets that can be included in your project:

target_link_libraries(<application> MPI::MPI_CXX DR::mpi)

See Distributed Ranges Tutorial for a live example of a cmake project that imports and uses Distributed Ranges.

Add below code to your main function to enable logging.

If using Single-Process model:

std::ofstream logfile("dr.log");
dr::drlog.set_file(logfile);

If using Multi-Process model:

int my_mpi_rank;
MPI_Comm_rank(MPI_COMM_WORLD, &my_mpi_rank);
std::ofstream logfile(fmt::format("dr.{}.log", my_mpi_rank));

Example of adding custom log statement to your code:

DRLOG("my debug message with varA:{} and varB:{}", a, b);

Contact us by writing a new issue.

We seek collaboration opportunities and welcome feedback on ways to extend the library, according to developer needs.

• CONTRIBUTING
• Fuzz Testing
• Spec Editing - Editing the API document
• Print Type - Print types at compile time:
• Testing - Test system maintenance
• Security - Security policy
• Doxygen